Statistical Models Commons^™

Forecasting Covid-19 With Temporal Hierarchies And Ensemble Methods, Li Shandross

Masters Theses

Infectious disease forecasting efforts underwent rapid growth during the COVID-19 pandemic, providing guidance for pandemic response and about potential future trends. Yet despite their importance, short-term forecasting models often struggled to produce accurate real-time predictions of this complex and rapidly changing system. This gap in accuracy persisted into the pandemic and warrants the exploration and testing of new methods to glean fresh insights.

In this work, we examined the application of the temporal hierarchical forecasting (THieF) methodology to probabilistic forecasts of COVID-19 incident hospital admissions in the United States. THieF is an innovative forecasting technique that aggregates time-series data into …

An Exploratory Statistical Method For Finding Interactions In A Large Dataset With An Application Toward Periodontal Diseases, Joshua Lambert

Theses and Dissertations--Epidemiology and Biostatistics

It is estimated that Periodontal Diseases effects up to 90% of the adult population. Given the complexity of the host environment, many factors contribute to expression of the disease. Age, Gender, Socioeconomic Status, Smoking Status, and Race/Ethnicity are all known risk factors, as well as a handful of known comorbidities. Certain vitamins and minerals have been shown to be protective for the disease, while some toxins and chemicals have been associated with an increased prevalence. The role of toxins, chemicals, vitamins, and minerals in relation to disease is believed to be complex and potentially modified by known risk factors. A …

Is Obesity Socially Contagious?, Ciani Jean Sparks

Statistics

The main objective of this paper is to analyze three different articles that discuss whether obesity could be socially contagious. According to the World Health Organization in 2013, obesity is the fifth leading risk for deaths around the world. This disease has dramatically increased in the last decade, which has led scientists to believe there are other factors contributing to the epidemic besides genetics. The first article I analyzed, written by Nicholas Christakis and James Fowler, provided a logistic regression model to estimate the odds of a person becoming obese. The model included the explanatory variables: age, sex, education, smoking …